Spectral analytic comparisons for data augmentation

The sandwich algorithm (SA) is an alternative to the data augmentation (DA) algorithm that uses an extra simulation step at each iteration. In this paper, we show that the sandwich algorithm always converges at least as fast as the DA algorithm, in the Markov operator norm sense. We also establish conditions under which the spectrum of SA dominates that of DA. An example illustrates the results.

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Year of publication:	2012
Authors:	Roy, Vivekananda
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 82.2012, 1, p. 103-108
Publisher:	Elsevier
Subject:	Compact operator \| Convergence rate \| Data augmentation algorithm \| Eigenvalue \| Markov chain

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010582237